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Journal of Chemical Information and Modeling

Dong Zheng, Yuming Gu, Xiang Li, Lizhu Zhang, Wei Zhao, Jing Ma
Realization of multi-stimuli responsiveness in one molecule remains a challenge due to the difficulty in understanding and control of comprehensive interplay between the external stimuli and the subtle conformation changes. The coexistence of dynamic bonding interactions, hydroxyl group and the azo chromophore in calcon causes the multi-stimuli responsiveness to external stimuli including temperature, pH-variation and light-irradiation. Density functional theory (DFT), time-dependent DFT (TDDFT), and various molecular dynamics (MD) simulations are employed to systematically investigate the azo-hydrazone tautomerism and E-Z isomerization...
February 15, 2019: Journal of Chemical Information and Modeling
Chunxing Ren, Xiaoxia Li, Li Guo
Understanding the underlying mechanisms on sensitivity-decrease of the CL-20/TNT co-crystal is essential for wide applications of the promising high-energetic CL-20. This work presents the chemical scenario of CL-20/TNT thermolysis obtained from ReaxFF molecular dynamics simulations. Facilitated by the unique VARxMD for reaction analysis, the interplay reactions between CL-20 and TNT responsible for the sensitivity-decrease of CL-20/TNT was first revealed. The early response of CL-20/TNT to thermal stimulus is dominated by N-NO2 bond cleavage for NO2 formation and C-N bond scission leading to ring-opening of CL-20...
February 15, 2019: Journal of Chemical Information and Modeling
Jingwei Weng, Wenning Wang
TolC is a channel protein responsible for substrate translocation across outer membrane, and it is also a part of the tripartite multidrug efflux pumps in Gram-negative bacteria. The crystal structure of TolC shows that the periplasmic entrance is tightly closed in the resting state, while substrate translocation definitely requires the entrance to open. How the occluded periplasmic entrance opens to allow passage of substrates remains elusive. In this work, we constructed a Markov state model from swarms of all-atom molecular dynamics (MD) simulation trajectories, which delineates the energetics of the conformational changes of TolC...
February 15, 2019: Journal of Chemical Information and Modeling
Michael Fernandez, Fuqiang Ban, Godwin Woo, Olexandr Isayev, Carl Perez, Valery V Fokin, Alexander Tropsha, Artem Cherkasov
In recent years, the field of quantitative structure-activity/property modeling (QSAR/QSPR) has developed into a stable technology capable of reliably predicting new bioactive molecules. With the availability of inexpensive commercial sources of both synthetic chemicals and bioactivity assays, a cheminformatics-savvy scientist can readily establish a virtual drug discovery enterprise. Not only a skilled computational chemist can develop a computer-aided drug discovery pipeline, but also acquire or have them made inexpensively for economic screen of desired on-target activity, critical off-target effects and essential drug-likeness properties...
February 15, 2019: Journal of Chemical Information and Modeling
Hong Zhang, Haohao Fu, Xueguang Shao, Francois Dehez, Christophe Chipot, Wensheng Cai
B- to A-DNA transition is known to be sensitive to the macroscopic properties of the solution, such as salt and ethanol concentrations. Microenvironmental effects on DNA conformational transition have been broadly studied. Providing an intuitive picture of how DNA responds to environmental changes is, however, still needed. Analyzing the chemical equilibrium of B-to-A DNA transition at critical concentrations, employing explicit-solvent simulations, is envisioned to help understand such microenvironmental effects...
February 15, 2019: Journal of Chemical Information and Modeling
Xiuming Li, Xin Yan, Qiong Gu, Huihao Zhou, Di Wu, Jun Xu
In the drug discovery process, unstable compounds in storage can lead to false positive or false negative bioassay conclusions. Prediction of the chemical stability of a compound by de novo methods is complex. Chemical instability prediction is commonly based on a model derived from empirical data. The COMDECOM (COMpound DECOMposition) project provides the empirical data for prediction of chemical stability. Models such as the extended-connectivity fingerprint and atom center fragments, were built from the COMDECOM data and used for estimation of chemical stability, but deficits in the existing models remain...
February 14, 2019: Journal of Chemical Information and Modeling
Giulia Palermo
CRISPR-Cas9 is a bacterial immune system with exciting applications for genome editing. In spite of extensive experimental characterization, the active site chemistry of the RuvC domain - which performs DNA cleavages - has remained elusive. Its knowledge is key for structure-based engineering aimed at improving DNA cleavages. Here, we deliver an in-depth characterization by using quantum-classical (QM/MM) molecular dynamics (MD) simulations and a Gaussian accelerated MD method, coupled with bioinformatics analysis...
February 14, 2019: Journal of Chemical Information and Modeling
Han Zhang, Wenjuan Jiang, Payal Chatterjee, Yun Luo
Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions...
February 14, 2019: Journal of Chemical Information and Modeling
Zijian Qin, Yao Xi, Shengde Zhang, Guiping Tu, Aixia Yan
This work reports the classification study conducted on the biggest COX-2 inhibitor dataset so far. Using 2925 diverse COX-2 inhibitors collected from 168 literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF) to develop twelve classification models. The best SVM and RF models resulted in MCC values of 0.73 and 0.72, respectively. The 2925 COX-2 inhibitors were reduced to a dataset of 1630 molecules by removing intermediately active inhibitors and twelve new classification models were constructed, yielding MCC values above 0...
February 14, 2019: Journal of Chemical Information and Modeling
Miha Skalic, José Jiménez Luna, Davide Sabbadin, Gianni De Fabritiis
In this work we propose a machine learning approach to generate novel molecules starting from a seed compound, its 3D shape and pharmacophoric features. The pipeline draws inspiration from generative models used in image analysis and represents a first example of de-novo design of lead-like molecules guided by shape-based features. A variational autoencoder is used to perturb the 3D representation of a compound followed by a system of convolutional and recurrent neural networks that generate a sequence of SMILES tokens...
February 14, 2019: Journal of Chemical Information and Modeling
Karolina Mikulska-Ruminska, Indira H Shrivastava, James Krieger, She Zhang, Hongchun Li, Hulya Bayir, Sally E Wenzel, Andrew P VanDemark, Valerian E Kagan, Ivet Bahar
Accurate modeling of structural dynamics of proteins and their differentiation across different species can help us understand generic mechanisms of function shared by family members and the molecular basis of the specificity of individual members. We focused here on the family of lipoxygenases, enzymes that catalyze lipid oxidation, the mammalian and bacterial structures of which have been elucidated. We present a systematic method of approach for characterizing the sequence, structure, dynamics and allosteric signaling properties of these enzymes using a combination of structure-based models and methods, and bioinformatics tools applied to a dataset of 88 structures...
February 14, 2019: Journal of Chemical Information and Modeling
Huali Cao, Jingxue Wang, Liping He, Yifei Qi, John Z H Zhang
Accurately predicting changes in protein stability due to mutations is important for protein engineering and for understanding the functional consequences of missense mutations in proteins. We have developed DeepDDG, a neural-network-based method, for use in the prediction of changes in the stability of proteins due to point mutations. The neural network was trained on more than 5700 manually curated experimental data points and was able to obtain a Pearson correlation coefficient of 0.48-0.56 for three independent test sets, which outperformed eleven other methods...
February 14, 2019: Journal of Chemical Information and Modeling
Joshua Staker, Kyle Marshall, Robert Abel, Carolyn Mae McQuaw
Chemical structure extraction from documents remains a hard problem due to both false positive identification of structures during segmentation and errors in the predicted structures. Current approaches rely on handcrafted rules and subroutines that perform reasonably well generally, but still routinely encounter situations where recognition rates are not yet satisfactory and systematic improvement is challenging. Complications impacting performance of current approaches include the diversity in visual styles used by various software to render structures, the frequent use of ad hoc annotations, and other challenges related to image quality, including resolution and noise...
February 13, 2019: Journal of Chemical Information and Modeling
Rasmus Leth, Bogac Ercig, Lars Olsen, Flemming Steen Jørgensen
Cytochrome P450 102A1 from Bacillus megaterium (BM3) is a fatty acid hydroxylase that has one of the highest turnover rates of any mono-oxygenase. Recent studies have shown how mutants of BM3 can produce metabolites of known drug compounds similar to those observed in humans. Single-point mutations in the binding pocket change the regioselective metabolism of fenamic acids from aromatic hydroxylation to aliphatic hydroxylation. This study is concerned with the individual contribution from accessibility and reactivity for drug metabolism with a future goal to develop fast methods for prediction...
February 13, 2019: Journal of Chemical Information and Modeling
Junchao Xia, William Flynn, Emilio Gallicchio, Keith Uplinger, Jonathan D Armstrong, Stefano Forli, Arthur J Olson, Ronald M Levy
To perform massive-scale replica exchange molecular dynamics (REMD) simulations for calculating binding free energies of protein-ligand complexes, we implemented the asynchronous replica exchange (AsyncRE) framework of the binding energy distribution analysis method (BEDAM) in implicit solvent on the IBM World Community Grid (WCG) and optimized the simulation parameters to reduce the overhead and improve the prediction power of the WCG AsyncRE simulations. We also performed the first massive-scale binding free energy calculations using the WCG distributed computing gird and 301 ligands from the SAMPL4 challenge for large-scale binding free energy predictions of HIV-1 integrase complexes...
February 13, 2019: Journal of Chemical Information and Modeling
Thomas Whitehead, Ben Irwin, Peter A Hunt, Matthew Segall, Gareth Conduit
We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays. In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches...
February 12, 2019: Journal of Chemical Information and Modeling
Nils-Ole Friedrich, Florian Flachsenberg, Agnes Meyder, Kai Sommer, Johannes Kirchmair, Matthias Rarey
Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0...
February 12, 2019: Journal of Chemical Information and Modeling
Victoria T Lim, Christopher I Bayly, Laszlo Fusti-Molnar, David L Mobley
Accurate hydrogen placement in molecular modeling is crucial for studying the interactions and dynamics of biomolecular systems. It is difficult to locate hydrogen atoms from many experimental structural characterization approaches, such as due to the weak scattering of x-ray radiation. Hydrogen atoms are usually added and positioned in silico when preparing experimental structures for modeling and simulation. The carboxyl functional group is a prototypical example of a functional group that requires protonation during structure preparation...
February 11, 2019: Journal of Chemical Information and Modeling
Joshua Horton, Alice E A Allen, Leela S Dodda, Daniel J Cole
Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. However, for molecules outside the training set, the parameters are potentially inaccurate and it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics...
February 11, 2019: Journal of Chemical Information and Modeling
Seyed Hossein Jamali, Ludger Wolff, Tim M Becker, Mariette de Groen, Mahinder Ramdin, Remco Hartkamp, André Bardow, Thijs J H Vlugt, Othonas A Moultos
We present a new plugin for LAMMPS for on-the-fly computation of transport properties (OCTP) in equilibrium molecular dynamics. OCTP computes the self- and Maxwell-Stefan diffusivities, bulk and shear viscosities, and thermal conductivities of pure fluids and mixtures in a single simulation. OCTP is the first implementation in LAMMPS that uses the Einstein relations combined with the order-n algorithm for the efficient sampling of dynamic variables. OCTP has low computational requirements and is easy to use because it follows the native input file format of LAMMPS...
February 11, 2019: Journal of Chemical Information and Modeling
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